Semi-Supervised Adaptive Parzen Gentleboost Algorithm for Fault Diagnosis

In this paper, we present a novel semi-supervised strategy for machine fault diagnosis. In the proposed method, we select parzen window as the generative classifier and Gentleboost as the discriminative classifier. Compared with SVM, boosting method has a very interesting property of relative immunity to overfitting. In addition, we propose a novel adaptive parzen window algorithm. It employs variational adaptive parzen window rather than a global optimized and fixed window, therefore, more accurate density estimates can be obtained. In experiments, artificial and machine vibration data are used to compare with other algorithms. Our proposed algorithm achieves stronger robustness and lower classification error rate.